Genomic selection of agronomic traits in hybrid rice using an NCII population

Rice
Yang XuZ Hu

Abstract

Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance. From the comparison of six GS methods, we found that predictabilities for different methods are significantly different, with genomic best linear unbiased prediction (GBLUP) and least absolute shrinkage and selection operation (LASSO) being the best, support vector machine (SVM) and partial least square (PLS) being the worst. The marker density has lower influence on predicting rice hybrid performance compared with the size of training population. Additionally, we used the 575 (115 × 5) hybrid rice as a training population to predict eight agronomic traits of all hybrids derived from 120 (115 + 5) rice varieties each mating with...Continue Reading

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Citations

Sep 20, 2019·G3 : Genes - Genomes - Genetics·Christina B AzodiShin-Han Shiu
Jan 18, 2019·PloS One·Isabela de Castro Sant' AnnaFrancyse Edith Chagas
Sep 22, 2020·Journal of Dairy Science·Jinyan TengZhe Zhang
Sep 24, 2020·Plants·Elisa CappettaMaria Raffaella Ercolano
Jun 3, 2021·Genes·Andrés J Cortés, Felipe López-Hernández
Nov 6, 2021·Communications Biology·Javaid Akhter BhatRajeev K Varshney

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Software Mentioned

BayesB
glmnet
GS
GBLUP
LASSO
BGLR
MAS
R package BGLR
Omega
R

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